Whole-genome sequencing projects are providing unprecedented information about human genetic variation. Polymorphisms abound in the human genome, in both coding and non-coding regions, but it remains a major challenge to associate genome variation with a functional consequence. There is growing awareness that genetic interactions, involving combinations of polymorphic alleles, must play a major role in determining phenotype. Yet, we have a limited understanding of how genetic variation translates into genetic interactions that affect an individual. One of the keys to solving this challenging problem will most certainly be an understanding of the general rules governing genetic networks, and how they are rewired in response to environmental or genetic perturbation. The budding yeast Saccharomyces cerevisiae has served as the pioneer model organism for virtually all genome-scale methods, and offers a unique format for exploring genetic networks. Our group developed the Synthetic Genetic Array (SGA) method, which automates yeast genetics and enables systematic analysis of genetic interactions. In the last grant period, we used the SGA method to complete a reference genetic interaction map for yeast, in standard growth conditions. The global network is rich in functional information, mapping a cellular wiring diagram of pleiotropy. Our analysis also revealed that a portion of the network was not mappable, with ~35% of query gene mutants exhibiting weak digenic genetic interaction profiles. These observations emphasize the need to survey genetic interactions in a condition-specific manner, to understand how genetic networks respond to genetic and other insults that may lead to disease states. AIM 1: Mapping condition-specific genetic networks on a genome-wide scale. We will use the SGA method to generate unbiased, genome-scale maps of genetic interactions across diverse conditions. Our systematic approach will generate the largest dynamic biological network of its kind, and will provide a resource to quantify environmental influences on genetic network structure. AIM 2: Global mapping of higher-order genetic interaction networks. We will map a network comprised of complex genetic interactions involving more than two genes. We will focus on hub genes, which are highly connected in the genetic network, and may act as general genetic modifiers. Modeling complex genetic interactions involving more than two genes will allow us to derive general rules governing genetic robustness and the relationship between genotype and phenotype. AIM 3: Quantification and analysis of condition-specific and higher-order genetic interactions. We will develop a computational framework for measuring genetic interactions across environments and genetic backgrounds, which will provide the basis for addressing several fundamental questions regarding the plasticity of genetic networks and the ability of higher-order genetic interactions t modulate complex phenotypes.